Kaiwen Zhao

Research / 2025 Materials Research Society (MRS) Fall Meeting & Exhibit

Early Prediction of Lithium-Ion Battery Degradation Using Health Indicators and Random Forest Regression

Kaiwen Zhao, Cole Younoszai, Linda Shi, Haodong Liu, Pedro Castilla

Boston, Massachusetts · Poster

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Figures

Analysis pipeline: cell testing through feature extraction to remaining-useful-life prediction, benchmarked against the NASA 18650 dataset.
Analysis pipeline: cell testing through feature extraction to remaining-useful-life prediction, benchmarked against the NASA 18650 dataset.
Capacity retention across 10,000 cycles, with logarithmic and exponential decay fits.
Capacity retention across 10,000 cycles, with logarithmic and exponential decay fits.
Normalised voltage against normalised capacity at 0.5C and 10C, with polynomial fits.
Normalised voltage against normalised capacity at 0.5C and 10C, with polynomial fits.

MRS Fall Meeting 2025 · CC BY 4.0

Citation

Zhao, K., Younoszai, C., Shi, L., Liu, H., & Castilla, P. (2025). Early Prediction of Lithium-Ion Battery Degradation Using Health Indicators and Random Forest Regression [Poster]. 2025 Materials Research Society (MRS) Fall Meeting & Exhibit, Boston, Massachusetts. https://doi.org/10.6084/m9.figshare.30970645

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